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Intelligent Electric Vehicle Charging Recommendation Based on Multi-Agent Reinforcement Learning [article]

Weijia Zhang, Hao Liu, Fan Wang, Tong Xu, Haoran Xin, Dejing Dou, Hui Xiong
2021 pre-print
In this paper, we propose a framework, named Multi-Agent Spatio-Temporal Reinforcement Learning (Master), for intelligently recommending public accessible charging stations by jointly considering various  ...  Specifically, by regarding each charging station as an individual agent, we formulate this problem as a multi-objective multi-agent reinforcement learning task.  ...  Along these lines, in this paper, we propose the Multi-Agent Spatio-Temporal Reinforcement Learning (Master) framework for intelligent charging recommendation.  ... 
doi:10.1145/3442381.3449934 arXiv:2102.07359v1 fatcat:a4owudk7drbnfkfamhn7cysj4e

CoordiQ : Coordinated Q-learning for Electric Vehicle Charging Recommendation [article]

Carter Blum, Hao Liu, Hui Xiong
2021 arXiv   pre-print
Electric vehicles have been rapidly increasing in usage, but stations to charge them have not always kept up with demand, so efficient routing of vehicles to stations is critical to operating at maximum  ...  Reinforcement learning offers a powerful paradigm for solving sequential decision-making problems, but traditional methods may struggle with sample efficiency due to the high number of possible actions  ...  Reinforcement Learning has been successfully applied to improving charging of electric vehicles.  ... 
arXiv:2102.00847v1 fatcat:vd3z3fhtdnagfggnqa5rqq3qji

Guest Editorial Introduction to the Special Issue on Intelligent Transportation Systems Empowered by AI Technologies

Seung-Hyun Kong, Yisheng Lv, Hai L. Vu, Juan-Carlos Cano, Jun-Won Choi, Dongsuk Kum, Brendan Tran Morris
2019 IEEE transactions on intelligent transportation systems (Print)  
The technical problem of limited drivable range and long charging duration has been the major hurdle for the popularization of electric vehicles (EVs), especially for commercial usage.  ...  There have been classification, deep learning, and reinforcement learning techniques, to name a few, which collectively have enabled almost all technical elements of the ITS.  ...  A Multi-Objective Agent-Based Control Approach With Application in Intelligent Traffic Signal System J. Jin and X.  ... 
doi:10.1109/tits.2019.2940856 fatcat:wrvsx6pgyfbjxlgnfekr7g7ovu

A Concise Review of Energy Management Strategies for Hybrid Energy Storage Systems

Bassey Etim Nyong-Bassey
2022 European Journal of Engineering and Technology Research  
The Reinforcement learning-based algorithm which uses an agent-based approach to optimally control the system offers an optimal solution for energy management.  ...  The energy management strategies were grouped into forecast/historical, heuristic logic, ANN-fuzzy logic, and reinforcement learning (machine learning) based methods.  ...  Interestingly, an intelligent agent-based algorithm, RL which can learn an MDP has been exploited mostly in literature for hybrid Electric vehicles while only a few have considered MGs.  ... 
doi:10.24018/ejeng.2022.7.3.2815 fatcat:hbjv3xf2yzdqberuik5cqcahry

Guest Editorial Introduction to the Special Issue on Deep Learning Models for Safe and Secure Intelligent Transportation Systems

Alireza Jolfaei, Neeraj Kumar, Min Chen, Krishna Kant
2021 IEEE transactions on intelligent transportation systems (Print)  
He is currently a Professor with the Department of Computer and Information Science, Temple University, Philadelphia, PA, USA, where he directs the IUCRC Center on Intelligent Storage.  ...  He has published in a wide variety of areas in computer science and authored a graduate textbook on performance modeling of computer systems.  ...  The proposed advertising strategy based on multi-agent reinforcement learning can train each digital billboard to switch different advertisements for different situations so that the influence of advertisements  ... 
doi:10.1109/tits.2021.3090721 fatcat:c2o2vno6bjbnxdn6y4zm7ztmvq

Machine learning approach towards remote diagnostics and repair of electric vehicle charging processes [chapter]

K. Renatus, A. Unger, B. Baker, O. Manicke
2021 ELIV 2021  
Various system parameters on both sides can cause states which lead to an incomplete charging sequence. An intelligent agent shall learn parameters aiming for a complete charging process.  ...  Electric vehicles have become a focus technology, promising a sustainable future mobility.  ...  In this emergent era of artificially intelligent (AI) solutions, an alternative question can be: (How) can electric vehicles learn to deal with, at least some, faulty charging behaviour?  ... 
doi:10.51202/9783181023846-163 fatcat:g547yys3wvfmbbvx4blsowyray

Guest Editorial: Introduction to the Special Section on Machine Learning-Based Internet of Vehicles: Theory, Methodology, and Applications

Jun Guo, Sunwoo Kim, Henk Wymeersch, Walid Saad, Wei Chen
2019 IEEE Transactions on Vehicular Technology  
Based on a multi-agent formulation, a centralized learning, decentralized deployment approach is taken.  ...  ., "Auction-Based Charging Scheduling With Deep Learning Framework for Multi-Drone Networks," they design a mechanism to control the charging schedule in a multi-drone setting.  ... 
doi:10.1109/tvt.2019.2914747 fatcat:rrpckr7cczfdzmqy7nkbcnsdua

2019 Index IEEE Transactions on Intelligent Transportation Systems Vol. 20

2019 IEEE transactions on intelligent transportation systems (Print)  
., +, TITS Oct. 2019 3688-3699 Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Pro- cesses.  ...  Ahmad, B.I., +, TITS April 2019 1278-1288 Autonomous aerial vehicles Deep Reinforcement Learning for Event-Driven Multi-Agent Decision Pro- cesses.  ... 
doi:10.1109/tits.2020.2966388 fatcat:xkvww7uabzhlzgfz3yiecyb75y

Improving Urban Mobility: using artificial intelligence and new technologies to connect supply and demand [article]

Ana L. C. Bazzan
2022 arXiv   pre-print
, agent-based simulation, among others.  ...  In this panorama, artificial intelligence plays an important role, especially with the advances in machine learning that translates in the use of computational vision, connected and autonomous vehicles  ...  Model Based Reinforcement Learning Approach When dealing with non-stationary environments, where vehicle flow is not constant, both model-independent reinforcement learning approaches and model-based ones  ... 
arXiv:2204.03570v1 fatcat:v4geolku7bd6nb5j4in7wsdf7i

Plug-in Electric Vehicle to Cloud Data Analytics for Charging Management

Muzakkir Hussain Md, Mohammad Saad Alam, Sufyan Beg M.M
2017 International Journal of Engineering and Technology  
Penetration of electric vehicle fleet into the existing charging infrastructure multiplies the load on the underlying grid system.  ...  This work demonstrates commercially viable multi-tier cloud enabled vehicle to cloud (V2C) smart fleet charging model for coordinating the charging of xEVs fleet that can support vehicle mobility satisfying  ...  based simulation of electric vehicles fleet charging strategies [9] and many more.  ... 
doi:10.21817/ijet/2017/v9i3/170903s056 fatcat:6c3konyo6rb5rjofa54246smsq

Reinforcement Learning Based EV Charging Management Systems – A review

Heba M. Abdullah, Adel Gastli, Lazhar Ben-Brahim
2021 IEEE Access  
Unlike other machine learning approaches, the RL technique is based on maximizing the cumulative reward.  ...  Among many existing model-free approaches, Reinforcement Learning (RL) has been widely used for EV charging management.  ...  Most of these studies relied their solutions on multi-agent RL techniques, while [90] and [100] used singleagent reinforcement-learning-based solutions.  ... 
doi:10.1109/access.2021.3064354 fatcat:ap66p3hnnng25dp65e6agsjmbq

Strategies for Controlling Microgrid Networks with Energy Storage Systems: A Review

Mudhafar Al-Saadi, Maher Al-Greer, Michael Short
2021 Energies  
Specific focus on control strategies based upon multiagent communication and reinforcement learning is a main objective of this paper, reflecting recent advancements in digitalization and AI.  ...  This paper presents a comprehensive review of decentralized, centralized, multiagent, and intelligent control strategies that have been proposed to control and manage distributed energy storage.  ...  Table 4 . 4 Summary of intelligent strategy-based reinforcement learning.  ... 
doi:10.3390/en14217234 fatcat:anflqasqkrfnnivu42vhiodbei

Intelligent Energy Management Systems for Electrified Vehicles: Current Status, Challenges, and Emerging Trends

Reihaneh Ostadian, John Ramoul, Atriya Biswas, Ali Emadi
2020 IEEE Open Journal of Vehicular Technology  
Index Terms-data-driven methods, electric vehicles, intelligent energy management strategy, reinforcement learning, powertrain architecture.  ...  Intelligent energy management strategies requirements are discussed in detail, and it is categorized to principle-based, data-driven, and composite-based methods.  ...  Charge depleting-charge sustaining (CDCS) and blended strategy are two main deterministic rule-based methods that are used for plug-in-hybrid electric vehicle (PHEV)s.  ... 
doi:10.1109/ojvt.2020.3018146 fatcat:7pqrnf52nze5fhpuyyhz4mjvtm

Scanning the Issue

Azim Eskandarian
2021 IEEE transactions on intelligent transportation systems (Print)  
The review includes work on deep learning techniques used in this application field.  ...  This article targets one such use case: Platooning is the efficient convoying of vehicles which makes use of selfdriving capabilities and inter-vehicle communication.  ...  A smoothly-convergent DRL (SCDRL) method is proposed based on the deep Q network (DQN) and reinforcement learning.  ... 
doi:10.1109/tits.2021.3113361 fatcat:x2xuoh2qefcdrmqpbwqfttcvhm

Reinforcement Learning for Ridesharing: An Extended Survey [article]

Zhiwei Qin, Hongtu Zhu, Jieping Ye
2022 arXiv   pre-print
Subsequently, we discuss a number of challenges and opportunities for reinforcement learning research on this important domain.  ...  In this paper, we present a comprehensive, in-depth survey of the literature on reinforcement learning approaches to decision optimization problems in a typical ridesharing system.  ...  In [110] , the RL agent determines both the price for each origin-destination (OD) pairs and the reposition/charging decisions for each electric vehicle in the fleet.  ... 
arXiv:2105.01099v3 fatcat:34whul4vnneo5k2pyvb4smhgz4
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